Caracterisation Agro-Morphologique Des Accessions D’arachide (Arachis Hypogaea L.) Pour La Teneur En Huile

In Niger, groundnut [Arachis hypogaea (L.)] is a leguminous crop cultivated in large area. Its seeds are used by oil industries. Groundnut oil is well appreciated by consumers notably in rural areas. The aim of this study was to select the accessions and taxa with high oil content and identify the oil content related traits. Thus, 20 accessions (9 local and 11 improved) belonging to 5 taxa types (FST, VUL, PRU, HYR, and HYB) were planted in randomized block design with three replications. Fifteen traits were investigated in field while oil content was quantified in laboratory. An analysis of variance was performed on all measured traits. Discriminant factorial analysis (AFD) was performed on taxon types, correlation between oil content and other traits was determined. Analysis revealed high and significant correlation between oil content and seed size. In the study, groundnut accessions ICG 8352 and ICG 9991 (oil content 52.8%) and taxon Fastigiata (oil content 48.52%) recorded highest oil content. Seed size and taxon type in groundnut could be criteria to select genotypes for oil industries.

Classification of Selected Essential Oil from Family Zingiberaceae Using E-Nose and Discriminant Factorial Analysis (DFA) Techniques: An Initial Study

Essential oils are very valuable natural resources and considered as secondary metabolites. They are produced from several parts of aromatic plant by using different type of extraction techniques. Each technique produced slightly different output oil yield and smell however they produced the same major chemicals compound markers when they are analysed using chemical analysis and profiling technique. Pure essential oils are known to have very strong odor and there are several techniques used to differentiate the volatile odor generated. In this study, Electronic Nose (E-Nose) technology is used to distinguish the smell among 8 samples selected within the same Zingiberaceae family. Their pattern recognition profiles were examined by statistical analysis using Discriminant Factorial Analysis (DFA). The result shows that the E-Nose technology combined with DFA were successfully discriminating all 8 samples within the same family with significant p-values < 0.05 across all samples and 100% recognition value.

Analysis of the Relationship between Banded Orographic Convection and Atmospheric Properties Using Factorial Discriminant Analysis and Neural Networks

The relationship between banded orographic convection and atmospheric properties is investigated for a region in the south of France where the associated rainfall events are thought to represent a significant portion of the hydrologic input. The purpose is to develop a method capable of producing an extensive database of banded orographic convection rainfall events from atmospheric sounding data for this region where insufficient rain gauge data and little or no suitable radar or satellite data are available. Two statistical methods—discriminant factorial analysis (DFA) and neural networks (NNs)—are used to determine 16 so-called elaborated nonlinear variables that best identify rainfall events related to banded orographic convection from atmospheric soundings. The approach takes rainfall information into account indirectly because it “learns” from the results of a previous study that explored meteorological and available rainfall databases, even if incomplete. The new variables include wind shear, low-level moisture fluxes, and gradients of the potential temperature in the lower layers of the atmosphere, and they were used to create an extensive database of banded orographic convection events from the archive of atmospheric soundings. Results of numerical simulations using the nonhydrostatic mesoscale (Méso-NH) meteorological model validate this approach and offer interesting perspectives for the understanding of the physical processes associated with banded orographic convection. DFA proves to be useful to determine the most discriminant factors with a physical meaning. Neural networks provide better results, but they do not allow for physical interpretation. The best solution is therefore to use the two methods together.

Inheritance and Heritability of Resistance to Citrus Leprosis

The genetic inheritance of resistance to leprosis, the most important viral disease of citrus in Brazil, was characterized through the phenotypic assessment of 143 hybrids resulting from crosses between tangor ‘Murcott’ (Citrus sinensis × C. reticulata) and sweet orange ‘Pêra’ (C. sinensis), considered to be resistant and susceptible to the disease, respectively. All plants were grafted onto Rangpur lime (C. limonia) and inoculated with Citrus leprosis virus, cytoplasmic type through the infestation with viruliferous mites, Brevipalpus phoenicis. The experiments were arranged in a completely randomized block design with 10 replicates. Incidence and severity of the disease in leaves and stems as well as plant growth parameters (plant height and stem diameter) were recorded for 3 years after the infestation with the viruliferous mites. The average values of all variables were analyzed using principal component analysis, discriminant factorial analysis, estimation of the clonal repeatability coefficients, and frequency of the distributions of the average values for each measured variable. The principal component analysis resulted in the identification of at least two groups with resistance and susceptibility to leprosis, respectively. About 99% of all hybrids were correctly classified according to the discriminant factorial analysis. The broad-sense heritability coefficients for characteristics associated with incidence and severity of leprosis ranged from 0.88 to 0.96. The data suggest that the inheritance of resistance to leprosis may be controlled by only a few genes.

Factors influencing the enlargement of trunk cankers of Phytophthoracinnamomi in red oak

Development of cankers on red oak (Quercusrubra L.) caused by Phytophthoracinnamomi Rands was studied with dendrochronological techniques. Factors of variation among lesions were investigated. Lesion parameters proved to vary with the particular tree to which the lesion belonged. A discriminant factorial analysis and principal component analysis showed that this tree susceptibility was made up of two components. The first accounted for root infection and wound healing. The second concerned susceptibility to vertical spread of the pathogen in bark tissues. Relationships between (i) tree susceptibility and (ii) tree age and vigor were studied. From the occurrence of the first lesion at the collar, number and severity of lesions increased with time, following an exponential and logistic curve, respectively. A comparison of ring widths in infected and healthy trees showed that the disease had no detrimental effect on tree growth. However, annual necrosis length was positively correlated with the annual radial tree growth. The observed differences among trees could not be explained by the effects of time and vigor only. Temperature and drought were analyzed to determine their effect on lesion development. No direct effect of drought on this disease could be shown. The low cold hardiness of P. cinnamomi in the trunk was determined.